class_docstr: Sums several losses into one.
  
  Args:
      num_inputs: number of input losses
      weights: a list of coefficient for merging losses
docstr: Returns definitions of module input ports.
          
docstr_tok: ["Returns","definitions","of","module","input","ports","."]
code: def input_types(self):
      """Returns definitions of module input ports."""
      input_types = {}
      for i in range(self._num_losses):
          input_types["loss_" + str(i + 1)] = NeuralType(
              elements_type=LossType()
          )
  
      return input_types
  
file_path: NeMo/nemo/collections/common/losses/aggregator.py
imports: from typing import List
  import torch
  from nemo.core.classes import Loss, typecheck
  from nemo.core.neural_types import LossType, NeuralType
label_desc: Use image node_id 1 for calling the AggregatorLoss obj's underlying member method code with example usage: obj.input_types() and returns: input_types